%global _empty_manifest_terminate_build 0 Name: python-stochatreat Version: 0.0.14 Release: 1 Summary: Stratified random assignment using pandas License: MIT License URL: https://github.com/manmartgarc/stochatreat Source0: https://mirrors.aliyun.com/pypi/web/packages/b8/fb/7c5557fbe821815cd5f9364fb20ed144448f59a87aac91d78baa171b9519/stochatreat-0.0.14.tar.gz BuildArch: noarch Requires: python3-pandas %description # Stochatreat ![Main Branch Tests](https://github.com/manmartgarc/stochatreat/actions/workflows/build.yml/badge.svg?branch=main) ## Introduction This is a Python tool to employ stratified randomization or sampling with uneven numbers in some strata using pandas. Mainly thought with RCTs in mind, it also works for any other scenario in where you would like to randomly allocate treatment within *blocks* or *strata*. The tool also supports having multiple treatments with different probability of assignment within each block or stratum. ## Installation ```bash pip install stochatreat ``` ## Usage Single cluster: ```python from stochatreat import stochatreat import numpy as np import pandas as pd # make 1000 households in 5 different neighborhoods. np.random.seed(42) df = pd.DataFrame( data={'id': list(range(1000)), 'nhood': np.random.randint(1, 6, size=1000)}) # randomly assign treatments by neighborhoods. treats = stochatreat( data=df, # your dataframe stratum_cols='nhood', # the blocking variable treats=2, # including control idx_col='id', # the unique id column random_state=42, # random seed misfit_strategy='stratum') # the misfit strategy to use # merge back with original data df = df.merge(treats, how='left', on='id') # check for allocations df.groupby('nhood')['treat'].value_counts().unstack() # previous code should return this treat 0 1 nhood 1 105 105 2 95 95 3 95 95 4 103 103 5 102 102 ``` Multiple clusters and treatment probabilities: ```python from stochatreat import stochatreat import numpy as np import pandas as pd # make 1000 households in 5 different neighborhoods, with a dummy indicator np.random.seed(42) df = pd.DataFrame(data={'id': list(range(1000)), 'nhood': np.random.randint(1, 6, size=1000), 'dummy': np.random.randint(0, 2, size=1000)}) # randomly assign treatments by neighborhoods and dummy status. treats = stochatreat(data=df, stratum_cols=['nhood', 'dummy'], treats=2, probs=[1/3, 2/3], idx_col='id', random_state=42, misfit_strategy='global') # merge back with original data df = df.merge(treats, how='left', on='id') # check for allocations df.groupby(['nhood', 'dummy'])['treat'].value_counts().unstack() # previous code should return this treat 0 1 nhood dummy 1 0 37 75 1 33 65 2 0 35 69 1 29 57 3 0 30 58 1 34 68 4 0 36 72 1 32 66 5 0 33 68 1 35 68 ``` ## Acknowledgments - `stochatreat` is totally inspired by [Alvaro Carril's](https://acarril.github.io/) fantastic Stata package: [`randtreat`](https://acarril.github.io/posts/randtreat), which was published in [The Stata Journal](https://www.stata-journal.com/article.html?article=st0490). - [David McKenzie's](http://blogs.worldbank.org/impactevaluations/tools-of-the-trade-doing-stratified-randomization-with-uneven-numbers-in-some-strata) fantastic post (and blog) about running RCTs for the World Bank. - [*In Pursuit of Balance: Randomization in Practice in Development Field Experiments.* Bruhn, McKenzie, 2009](https://www.aeaweb.org/articles?id=10.1257/app.1.4.200) %package -n python3-stochatreat Summary: Stratified random assignment using pandas Provides: python-stochatreat BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-stochatreat # Stochatreat ![Main Branch Tests](https://github.com/manmartgarc/stochatreat/actions/workflows/build.yml/badge.svg?branch=main) ## Introduction This is a Python tool to employ stratified randomization or sampling with uneven numbers in some strata using pandas. Mainly thought with RCTs in mind, it also works for any other scenario in where you would like to randomly allocate treatment within *blocks* or *strata*. The tool also supports having multiple treatments with different probability of assignment within each block or stratum. ## Installation ```bash pip install stochatreat ``` ## Usage Single cluster: ```python from stochatreat import stochatreat import numpy as np import pandas as pd # make 1000 households in 5 different neighborhoods. np.random.seed(42) df = pd.DataFrame( data={'id': list(range(1000)), 'nhood': np.random.randint(1, 6, size=1000)}) # randomly assign treatments by neighborhoods. treats = stochatreat( data=df, # your dataframe stratum_cols='nhood', # the blocking variable treats=2, # including control idx_col='id', # the unique id column random_state=42, # random seed misfit_strategy='stratum') # the misfit strategy to use # merge back with original data df = df.merge(treats, how='left', on='id') # check for allocations df.groupby('nhood')['treat'].value_counts().unstack() # previous code should return this treat 0 1 nhood 1 105 105 2 95 95 3 95 95 4 103 103 5 102 102 ``` Multiple clusters and treatment probabilities: ```python from stochatreat import stochatreat import numpy as np import pandas as pd # make 1000 households in 5 different neighborhoods, with a dummy indicator np.random.seed(42) df = pd.DataFrame(data={'id': list(range(1000)), 'nhood': np.random.randint(1, 6, size=1000), 'dummy': np.random.randint(0, 2, size=1000)}) # randomly assign treatments by neighborhoods and dummy status. treats = stochatreat(data=df, stratum_cols=['nhood', 'dummy'], treats=2, probs=[1/3, 2/3], idx_col='id', random_state=42, misfit_strategy='global') # merge back with original data df = df.merge(treats, how='left', on='id') # check for allocations df.groupby(['nhood', 'dummy'])['treat'].value_counts().unstack() # previous code should return this treat 0 1 nhood dummy 1 0 37 75 1 33 65 2 0 35 69 1 29 57 3 0 30 58 1 34 68 4 0 36 72 1 32 66 5 0 33 68 1 35 68 ``` ## Acknowledgments - `stochatreat` is totally inspired by [Alvaro Carril's](https://acarril.github.io/) fantastic Stata package: [`randtreat`](https://acarril.github.io/posts/randtreat), which was published in [The Stata Journal](https://www.stata-journal.com/article.html?article=st0490). - [David McKenzie's](http://blogs.worldbank.org/impactevaluations/tools-of-the-trade-doing-stratified-randomization-with-uneven-numbers-in-some-strata) fantastic post (and blog) about running RCTs for the World Bank. - [*In Pursuit of Balance: Randomization in Practice in Development Field Experiments.* Bruhn, McKenzie, 2009](https://www.aeaweb.org/articles?id=10.1257/app.1.4.200) %package help Summary: Development documents and examples for stochatreat Provides: python3-stochatreat-doc %description help # Stochatreat ![Main Branch Tests](https://github.com/manmartgarc/stochatreat/actions/workflows/build.yml/badge.svg?branch=main) ## Introduction This is a Python tool to employ stratified randomization or sampling with uneven numbers in some strata using pandas. Mainly thought with RCTs in mind, it also works for any other scenario in where you would like to randomly allocate treatment within *blocks* or *strata*. The tool also supports having multiple treatments with different probability of assignment within each block or stratum. ## Installation ```bash pip install stochatreat ``` ## Usage Single cluster: ```python from stochatreat import stochatreat import numpy as np import pandas as pd # make 1000 households in 5 different neighborhoods. np.random.seed(42) df = pd.DataFrame( data={'id': list(range(1000)), 'nhood': np.random.randint(1, 6, size=1000)}) # randomly assign treatments by neighborhoods. treats = stochatreat( data=df, # your dataframe stratum_cols='nhood', # the blocking variable treats=2, # including control idx_col='id', # the unique id column random_state=42, # random seed misfit_strategy='stratum') # the misfit strategy to use # merge back with original data df = df.merge(treats, how='left', on='id') # check for allocations df.groupby('nhood')['treat'].value_counts().unstack() # previous code should return this treat 0 1 nhood 1 105 105 2 95 95 3 95 95 4 103 103 5 102 102 ``` Multiple clusters and treatment probabilities: ```python from stochatreat import stochatreat import numpy as np import pandas as pd # make 1000 households in 5 different neighborhoods, with a dummy indicator np.random.seed(42) df = pd.DataFrame(data={'id': list(range(1000)), 'nhood': np.random.randint(1, 6, size=1000), 'dummy': np.random.randint(0, 2, size=1000)}) # randomly assign treatments by neighborhoods and dummy status. treats = stochatreat(data=df, stratum_cols=['nhood', 'dummy'], treats=2, probs=[1/3, 2/3], idx_col='id', random_state=42, misfit_strategy='global') # merge back with original data df = df.merge(treats, how='left', on='id') # check for allocations df.groupby(['nhood', 'dummy'])['treat'].value_counts().unstack() # previous code should return this treat 0 1 nhood dummy 1 0 37 75 1 33 65 2 0 35 69 1 29 57 3 0 30 58 1 34 68 4 0 36 72 1 32 66 5 0 33 68 1 35 68 ``` ## Acknowledgments - `stochatreat` is totally inspired by [Alvaro Carril's](https://acarril.github.io/) fantastic Stata package: [`randtreat`](https://acarril.github.io/posts/randtreat), which was published in [The Stata Journal](https://www.stata-journal.com/article.html?article=st0490). - [David McKenzie's](http://blogs.worldbank.org/impactevaluations/tools-of-the-trade-doing-stratified-randomization-with-uneven-numbers-in-some-strata) fantastic post (and blog) about running RCTs for the World Bank. - [*In Pursuit of Balance: Randomization in Practice in Development Field Experiments.* Bruhn, McKenzie, 2009](https://www.aeaweb.org/articles?id=10.1257/app.1.4.200) %prep %autosetup -n stochatreat-0.0.14 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-stochatreat -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Jun 20 2023 Python_Bot - 0.0.14-1 - Package Spec generated